BACKGROUND: Cardiac channelopathies such as catecholaminergic polymorphic tachycardia and long QT syndrome predispose patients to fatal arrhythmias and sudden cardiac death. As genetic testing has become common in clinical practice, variants of uncertain significance (VUS) in genes associated with catecholaminergic polymorphic ventricular tachycardia and long QT syndrome are frequently found. The objective of this study was to predict pathogenicity of catecholaminergic polymorphic ventricular tachycardia-associated RYR2 VUS and long QT syndrome-associated VUS in KCNQ1 , KCNH2 , and SCN5A by developing gene-specific machine learning models and assessing them using cross-validation, cellular electrophysiological data, and clinical correlation. METHODS: The GENe-specific EnSemble grId Search framework was developed to identify high-performing machine learning models for RYR2 , KCNQ1 , KCNH2 , and SCN5A using variant- and protein-specific inputs. Final models were applied to datasets of VUS identified from ClinVar and exome sequencing. Whole cell patch clamp and clinical correlation of selected VUS was performed. RESULTS: The GENe-specific EnSemble grId Search models outperformed alternative methods, with area under the receiver operating characteristics up to 0.87, average precisions up to 0.83, and calibration slopes as close to 1.0 (perfect) as 1.04. Blinded voltage-clamp analysis of HEK293T cells expressing 2 predicted pathogenic variants in KCNQ1 each revealed an ≈80% reduction of peak Kv7.1 current compared with WT. Normal Kv7.1 function was observed in KCNQ1-V241I HEK cells as predicted. Though predicted benign, loss of Kv7.1 function was observed for KCNQ1-V106D HEK cells. Clinical correlation of 9/10 variants supported model predictions. CONCLUSIONS: Gene-specific machine learning models may have a role in post-genetic testing diagnostic analyses by providing high performance prediction of variant pathogenicity.

Background: Accurately determining variant pathogenicity is critical in the diagnosis of cardiac channelopathies; however, it remains unknown how variant pathogenicity status changes over time. Our aim is to use a comprehensive analysis of ClinVar to understand mutability of variant evaluation in channelopathy-associated genes to inform clinical decision-making around variant calling. Methods: We identified 10 genes ( RYR2, CASQ2, KCNQ1, KCNH2, SCN5A, CACNA1C, CALM1, CALM2, CALM3, TRDN ) strongly associated with cardiac channelopathies, as well as 3 comparison gene sets (disputed long QT syndrome, sudden unexpected death in epilepsy, and all ClinVar). We comprehensively analyzed variant pathogenicity calls over time using the ClinVar database with Rstudio. Analyses focused on the frequency and directionality of clinically meaningful changes in disease association, defined as a change from one of the following three categories to another: likely benign/benign, conflicting evidence of pathogenicity/variant of uncertain significance, and likely pathogenic/pathogenic. Results: In total, among channelopathy-associated genes, there were 9975 variants in ClinVar and 8.4% had a clinically meaningful change in disease association at least once over the past 10 years, as opposed to 4.9% of all ClinVar variants. The 3 channelopathy-associated genes with the most variants undergoing a clinically significant change were KCNQ1 (20.9%) , SCN5A (11.2%), and KCNH2 (10.1%). Ten of the 12 included genes had variant evaluations that trended toward diagnostic uncertainty over time. Specifically, channelopathy-associated gene variants with either pathogenic/likely pathogenic or benign/likely benign assignments were 5.6× and 2×, respectively, as likely to be reevaluated to conflicting/variant of uncertain significance compared to the converse. Conclusions: Over the past 10 years, 8.4% of variants in channelopathy-associated genes have changed pathogenicity status with a decline in overall diagnostic certainty. Ongoing clinical and genetic variant follow-up is needed to account for presence of clinically meaningful change in variant pathogenicity assignment over time.

Background As utilization of clinical exome sequencing (ES) has expanded, criteria for evaluating the diagnostic weight of incidentally identified variants are critical to guide clinicians and researchers. This is particularly important in genes associated with dilated cardiomyopathy (DCM), which can cause heart failure and sudden death. We sought to compare the frequency and distribution of incidentally identified variants in DCM‐associated genes between a clinical referral cohort with those in control and known case cohorts to determine the likelihood of pathogenicity among those undergoing genetic testing for non‐DCM indications. Methods and Results A total of 39 rare, non‐ TTN DCM‐associated genes were identified and evaluated from a clinical ES testing referral cohort (n=14 005, Baylor Genetic Laboratories) and compared with a DCM case cohort (n=9442) as well as a control cohort of population variants (n=141 456) derived from the gnomAD database. Variant frequencies in each cohort were compared. Signal‐to‐noise ratios were calculated comparing the DCM and ES cohort with the gnomAD cohort. The likely pathogenic/pathogenic variant yield in the DCM cohort (8.2%) was significantly higher than in the ES cohort (1.9%). Based on signal‐to‐noise and correlation analysis, incidental variants found in FLNC , RBM20 , MYH6 , DSP , ABCC9 , JPH2 , and NEXN had the greatest chance of being DCM‐associated. Conclusions The distribution of pathogenic variants between the ES cohort and the DCM case cohort was gene specific, and variants found in the ES cohort were similar to variants found in the control cohort. Incidentally identified variants in specific genes are more associated with DCM than others.

Background Pathogenic variation in the ATP1A3 ‐encoded sodium‐potassium ATPase, ATP1A3, is responsible for alternating hemiplegia of childhood (AHC). Although these patients experience a high rate of sudden unexpected death in epilepsy, the pathophysiologic basis for this risk remains unknown. The objective was to determine the role of ATP1A3 genetic variants on cardiac outcomes as determined by QT and corrected QT (QTc) measurements. Methods and Results We analyzed 12‐lead ECG recordings from 62 patients (male subjects=31, female subjects=31) referred for AHC evaluation. Patients were grouped according to AHC presentation (typical versus atypical), ATP1A3 variant status (positive versus negative), and ATP1A3 variant (D801N versus other variants). Manual remeasurements of QT intervals and QTc calculations were performed by 2 pediatric electrophysiologists. QTc measurements were significantly shorter in patients with positive ATP1A3 variant status ( P <0.001) than in patients with genotype‐negative status, and significantly shorter in patients with the ATP1A3‐D801N variant than patients with other variants ( P <0.001). The mean QTc for ATP1A3‐D801N was 344.9 milliseconds, which varied little with age, and remained <370 milliseconds throughout adulthood. ATP1A3 genotype status was significantly associated with shortened QTc by multivariant regression analysis. Two patients with the ATP1A3‐D801N variant experienced ventricular fibrillation, resulting in death in 1 patient. Rare variants in ATP1A3 were identified in a large cohort of genotype‐negative patients referred for arrhythmia and sudden unexplained death. Conclusions Patients with AHC who carry the ATP1A3‐D801N variant have significantly shorter QTc intervals and an increased likelihood of experiencing bradycardia associated with life‐threatening arrhythmias. ATP1A3 variants may represent an independent cause of sudden unexplained death. Patients with AHC should be evaluated to identify risk of sudden death.

Background: Sudden infant death syndrome (SIDS) is the sudden, unexplained death of infants &lt;1 year old. SIDS remains a leading cause of death in US infants. We aim to identify associations between SIDS and race/ethnicity, birth weight/gestational age, and socioeconomic/environmental factors in North Carolina (NC) to help identify infants at risk for SIDS.Methods and Results: In this IRB-approved study, infant mortality 2007–2016 and death certificate-linked natality 2007–2014 were obtained from the NC Department of Health and Human Services. General, NC natality statistics 2007–2016 were obtained from CDC Wonder. Association between SIDS/total infant death and covariates (below) were calculated. Total infant mortality decreased 2007–2016 by an average of 14 deaths/100,000 live births per year, while SIDS incidence remained constant. Risk ratios of SIDS/total infant deaths, standardized to Non-Hispanic White, were 1.76/2.41 for Non-Hispanic Black and 0.49/0.97 for Hispanic infants. Increased SIDS risk was significantly and independently associated with male infant sex, Non-Hispanic Black maternal race/ethnicity, young maternal age, low prenatal care, gestational age &lt;39 weeks, birthweight &lt;2500 g, low maternal education, and maternal tobacco use (p &lt; 0.01). Maternal previous children now deceased also trended toward association with increased SIDS risk.Conclusions: A thorough SIDS risk assessment should include maternal, socioeconomic, and environmental risk factors as these are associated with SIDS in our population.